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A model for the identification of the functional structures of unstructured abstracts in the social sciences

A model for the identification of the functional structures of unstructured abstracts in the... Reorganising unstructured academic abstracts according to a certain logical structure can help scholars not only extract valid information quickly but also facilitate the faceted search of academic literature. This study aims to build a high-performance model for identifying of the functional structures of unstructured abstracts in the social sciences.Design/methodology/approachThis study first investigated the structuring of abstracts in academic articles in the field of social sciences, using large-scale statistical analyses. Then, the functional structures of sentences in the abstract in a corpus of more than 3.5 million abstracts were identified from sentence classification and sequence tagging by using several models based on either machine learning or a deep learning approach, and the results were compared.FindingsThe results demonstrate that the functional structures of sentences in abstracts in social science manuscripts include the background, purpose, methods, results and conclusions. The experimental results show that the bidirectional encoder representation from transformers exhibited the best performance, the overall F1 score of which was 86.23%.Originality/valueThe data set of annotated social science abstract is generated and corresponding models are trained on the basis of the data set, both of which are available on Github (https://github.com/Academic-Abstract-Knowledge-Mining/SSCI_Abstract_Structures_Identification). Based on the optimised model, a Web application for the identification of the functional structures of abstracts and their faceted search in social sciences was constructed to enable rapid and convenient reading, organisation and fine-grained retrieval of academic abstracts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Electronic Library Emerald Publishing

A model for the identification of the functional structures of unstructured abstracts in the social sciences

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0264-0473
eISSN
0264-0473
DOI
10.1108/el-10-2021-0190
Publisher site
See Article on Publisher Site

Abstract

Reorganising unstructured academic abstracts according to a certain logical structure can help scholars not only extract valid information quickly but also facilitate the faceted search of academic literature. This study aims to build a high-performance model for identifying of the functional structures of unstructured abstracts in the social sciences.Design/methodology/approachThis study first investigated the structuring of abstracts in academic articles in the field of social sciences, using large-scale statistical analyses. Then, the functional structures of sentences in the abstract in a corpus of more than 3.5 million abstracts were identified from sentence classification and sequence tagging by using several models based on either machine learning or a deep learning approach, and the results were compared.FindingsThe results demonstrate that the functional structures of sentences in abstracts in social science manuscripts include the background, purpose, methods, results and conclusions. The experimental results show that the bidirectional encoder representation from transformers exhibited the best performance, the overall F1 score of which was 86.23%.Originality/valueThe data set of annotated social science abstract is generated and corresponding models are trained on the basis of the data set, both of which are available on Github (https://github.com/Academic-Abstract-Knowledge-Mining/SSCI_Abstract_Structures_Identification). Based on the optimised model, a Web application for the identification of the functional structures of abstracts and their faceted search in social sciences was constructed to enable rapid and convenient reading, organisation and fine-grained retrieval of academic abstracts.

Journal

The Electronic Library – Emerald Publishing

Published: Nov 29, 2022

Keywords: Social science identification of functional structures of abstracts; Deep learning bidirectional encoder representation from transformers (BERT); Academic search; Searching; Abstracts; Functional structures

References

  • Coherent citation-based summarization of scientific papers
    Abu-Jbara, A.; Radev, D.
  • Automatic zone identification in scientific papers via fusion techniques
    Asadi, N.; Badie, K.; Mahmoudi, M.T.
  • Analysing genre: language use in professional settings
    Bhatia, V.K.
  • Neural networks for joint sentence classification in medical paper abstracts
    Dernoncourt, F.; Lee, J.Y.; Szolovits, P.
  • BERT: pre-training of deep bidirectional transformers for language understanding
    Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K.
  • Gate-variants of gated recurrent unit (GRU) neural networks
    Dey, R.; Salemt, F.M.
  • The distribution of references across texts: some implications for citation analysis
    Ding, Y.; Liu, X.; Guo, C.; Cronin, B.
  • Ensemble-style self-training on citation classification
    Dong, C.; Schäfer, U.
  • Context sensitive article ranking with citation context analysis
    Doslu, M.; Haluk, O.B.
  • Structured summarization of academic publications
    Gidiotis, A.; Tsoumakas, G.
  • A deep learning classifier for sentence classification in biomedical and computer science abstracts
    Gonçalves, S.; Cortez, P.; Moro, S.
  • Teaching EFL students to extract structural information from abstracts
    Graetz, N.
  • Math-word embedding in math search and semantic extraction
    Greiner-Petter, A.; Youssef, A.; Ruas, T.; Miller, B.R.; Schubotz, M.; Aizawa, A.; Gipp, B.
  • Weakly supervised learning of information structure of scientific abstracts – is it accurate enough to benefit real-world tasks in biomedicine?
    Guo, Y.; Korhonen, A.; Silins, I.; Stenius, U.
  • Identifying the information structure of scientific abstracts: an investigation of three different schemes
    Guo, Y.; Korhonen, A.; Liakata, M.; Silins, I.; Sun, L.; Stenius, U.
  • Identifying problems and solutions in scientific text
    Heffernan, K.; Teufel, S.
  • Identifying sections in scientific abstracts using conditional random fields
    Hirohata, K.; Okazaki, N.; Ananiadou, S.; Ishizuka, M.
  • Long short-term memory
    Hochreiter, S.; Schmidhuber, J.
  • Where are citations located in the body of scientific articles? A study of the distributions of citation locations
    Hu, Z.G.; Chen, C.M.; Liu, Z.Y.
  • CODA-19: using a non-expert crowd to annotate research aspects on 10,000+ abstracts in the COVID-19 open research dataset
    Huang, T.H.K.; Huang, C.Y.; Ding, C.K.C.; Hsu, Y.C.; Giles, C.L.
  • Bidirectional LSTM-CRF models for sequence tagging
    Huang, Z.; Xu, W.; Yu, K.
  • Insights from CL-SciSumm 2016: the faceted scientific document summarization shared task
    Jaidka, K.; Chandrasekaran, M.K.; Rustagi, S.; Kan, M.Y.
  • Hierarchical neural networks for sequential sentence classification in medical scientific abstracts
    Jin, D.; Szolovits, P.
  • Automatic classification of sentences to support evidence based medicine
    Kim, S.N.; Martinez, D.; Cavedon, L.; Yencken, L.
  • Convolutional neural networks for sentence classification
    Kim, Y.
  • CRF++: yet another CRF toolkit
    Kudo, T.
  • Result identification for biomedical abstracts using conditional random fields
    Lin, R.T.; Dai, H.J.; Bow, Y.Y.; Day, M.Y.; Tsai, R.T.H.; Hsu, W.L.
  • Abstract sentence classification for scientific papers based on transductive SVM
    Liu, Y.; Wu, F.; Liu, M.; Liu, B.
  • Literature retrieval based on citation context
    Liu, S.; Chen, C.; Ding, K.; Wang, B.; Xu, K.; Lin, Y.
  • Functional structure identification of scientific documents in computer science
    Lu, W.; Huang, Y.; Bu, Y.; Cheng, Q.
  • Categorization of sentence types in medical abstracts
    McKnight, L.; Srinivasan, P.
  • The Stanford CoreNLP natural language processing toolkit
    Manning, C.D.; Surdeanu, M.; Bauer, J.; Finkel, J.R.; Bethard, S.; McClosky, D.
  • Bringing structure into summaries: a faceted summarization dataset for long scientific documents
    Meng, R.; Thaker, K.; Zhang, L.; Dong, Y.; Yuan, X.; Wang, T.; He, D.
  • Structuralizing biomedical abstracts with discriminative linguistic features
    Nam, S.; Kim, S.K.; Kim, H.G.; Ngo, V.; Zong, N.
  • Scikit-learn: machine learning in python
    Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.
  • Using discourse analysis to improve text categorization in MEDLINE
    Ruch, P.; Geissbühler, A.; Gobeill, J.; Lisacek, F.; Tbahriti, I.; Veuthey, A.L.; Aronson, A.R.
  • Bibliometric-enhanced information retrieval: a novel deep feature engineering approach for algorithm searching from full-text publications
    Safder, I.; Hassan, S.U.
  • Scientific document summarization using citation context and multi-objective optimization
    Saini, N.; Kumar, S.; Saha, S.; Bhattacharyya, P.
  • The introduction, methods, results, and discussion (IMRAD) structure: a fifty-year survey
    Sollaci, L.B.; Pereira, M.G.
  • Using argumentation to retrieve articles with similar citations: an inquiry into improving related articles search in the MEDLINE digital library
    Tbahriti, I.; Chichester, C.; Lisacek, F.; Ruch, P.
  • Summarizing scientific articles: experiments with relevance and rhetorical status
    Teufel, S.; Moens, M.
  • Automatic classification of citation function
    Teufel, S.; Siddharthan, A.; Tidhar, D.
  • A sentence classification system for multi-document summarization in the biomedical domain
    Yamamoto, Y.; Takagi, T.
  • Using the argumentative structure of scientific literature to improve information access
    Yepes, A.J.; Mork, J.G.; Aronson, A.R.
  • Cited text span identification for scientific summarisation using pre-trained encoders
    Zerva, C.; Nghiem, M.Q.; Nguyen, N.T.; Ananiadou, S.
  • Grasping the structure of journal articles: utilizing the functions of information units
    Zhang, L.